Image Processing and IoT Based Innovative Energy Conservation Technique
Bibliographic record
Abstract
This paper illustrates an innovative, real-time, energy monitoring system in educational institutions, using MATLAB, image acquisition and processing mechanism. Smart Innovative Method for Energy (E-SIM) conservation proposed here, designs, deploys and evaluates energy consumption patterns of laboratories, lecture theaters and halls in institutions. Specially designed hardware is used to monitor energy consumption pattern of each laboratory or lecture hall. The data is then matched with the time-table and occupancy level of that laboratory or lecture hall using cloud based data analytics and IoT (Internet of Things) in real time. If the energy consumption doesn't match the time-table or the occupancy level, an alert is generated for further investigation and action. Matching energy consumption patterns with the time-table of laboratories and lecture halls in an educational institution over a period of time can result in significant energy saving. The E-SIM may help institutes design cost-effective recommendations to address energy inefficiencies and implement new initiatives. The complete design includes energy infrastructure (metered laboratories and lecture theaters), energy routing within institutes, web applications and data analytics. The E-SIM helps monitor the utilization of energy in organizations resulting in efficient energy management by them thereby reducing their environmental impact. By monitoring plug loads in each laboratory or lecture theatre, the number of devices using energy, the number of occupants and the actual energy use can be better managed. This is the vital information required to take actions and policy decisions for saving energy and it may, in future, provide the real time on-line digital ability to on-off control at each plug.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".